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首页> 外文期刊>Information visualization >Animated interval scatter-plot views for the exploratory analysis of large-scale microarray time-course data
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Animated interval scatter-plot views for the exploratory analysis of large-scale microarray time-course data

机译：动画间隔散点图视图，用于大规模微阵列时程数据的探索性分析

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Microarray technologies are a relatively new development that allow biologists to monitor the activity of thousands of genes (normally around 8,000) in parallel across multiple stages of a biological process. While this new perspective on biological functioning is recognised as having the potential to have a significant impact on the diagnosis, treatment, and prevention of diseases, it is only through effective analysis of the data produced that biologists can begin to unlock this potential. A significant obstacle to achieving effective analysis of microarray time-course is the combined scale and complexity of the data. This inevitably makes it difficult to reveal certain significant patterns in the data. In particular, it is less dominant patterns and, specifically, patterns that occur over smaller intervals of an experiment's overall time-frame that are more difficult to find. While existing techniques are capable of finding either unexpected patterns of activity over the majority of an experiment's time-frame or expected patterns of activity over smaller intervals of the time-frame, there are no techniques, or combination of techniques, that are suitable for finding unsuspected patterns of activity over smaller intervals. In order to overcome this limitation we have developed the Time-series Explorer, which specifically supports biologists in their attempts to reveal these types of pattern by allowing them to control an animated interval scatter-plot view of their data. This paper discusses aspects of the technique that make such an animated overview viable and describes the results of a user evaluation assessing the practical utility of the technique within the wider context of microarray time-series analysis as a whole.
机译：微阵列技术是一个相对较新的发展，它使生物学家可以跨生物学过程的多个阶段并行监视数千个基因（通常约为8,000个）的活性。尽管人们对生物学功能的这种新观点具有对疾病的诊断，治疗和预防产生重大影响的潜力，但只有通过对产生的数据进行有效分析，生物学家才能开始释放这种潜力。实现微阵列时间过程的有效分析的主要障碍是数据的规模和复杂性。这不可避免地使得很难揭示数据中的某些重要模式。特别是，较少占主导地位的模式，尤其是在整个实验时间范围较小的间隔内出现的模式，较难找到。虽然现有技术能够在实验的大部分时间范围内找到意外的活动模式，或者在较小的时间间隔内找到预期的活动模式，但没有适合于发现的技术或技术组合在较小的时间间隔内的意外活动模式。为了克服此限制，我们开发了“时间序列浏览器”，它特别支持生物学家尝试通过允许他们控制数据的动画间隔散点图视图来揭示这些类型的模式。本文讨论了使这种动画效果可行的技术的各个方面，并描述了在整个微阵列时间序列分析的更广泛范围内评估该技术的实用性的用户评估结果。

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